#!/usr/bin/python
# -*- coding:utf-8 -*-
from numpy import *

def loadDataSet():
    postingList = [['my', 'dog', 'has', 'flea', \
                    'problems', 'help', 'please'],
                   ['maybe', 'not', 'take', 'him', \
                    'to', 'dog', 'park', 'stupid'],
                   ['my', 'dalmation', 'is', 'so', 'cute', \
                    'I', 'love', 'him'],
                   ['stop', 'posting', 'stupid', 'worthless', 'garbage'],
                   ['mr', 'licks', 'ate', 'my', 'steak', 'how', \
                    'to', 'stop', 'him'],
                   ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
    classVec = [0,1,0,1,0,1]#1代表侮辱性文字，0代表正常言论
    return postingList,classVec

def createVocabList(dataSet):
    vocabSet = set([])
    for document in dataSet:
        vocabSet = vocabSet | set(document)
    return list(vocabSet)

def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
           returnVec[vocabList.index(word)] = 1
        else:
           print "the word: %s is not in my Vocabulary!" %word
    return returnVec

def bagOfWords2Vec(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
           returnVec[vocabList.index(word)] += 1
    return returnVec

def trainNB0(trainMatrix,trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCategory)/float(numTrainDocs)
    p0Num = ones(numWords);p1Num=ones(numWords)
    p0Denom = 2.0;p1Denom = 2.0
    for i in range(numTrainDocs):
        if trainCategory[i] == 1:
           p1Num += trainMatrix[i]
           p1Denom += sum(trainMatrix[i])
        else:
           p0Num += trainMatrix[i]
           p0Denom += sum(trainMatrix[i])
    p1Vect = log(p1Num/p1Denom)
    p0Vect = log(p0Num/p0Denom)
    return p0Vect,p1Vect,pAbusive

def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
    p1 = sum(vec2Classify * p1Vec) + log(pClass1)
    p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
    if p1 > p0:
       return 1
    else:
       return 0

def testingNB():
    listOPosts, listClasses = loadDataSet()
    myVocabList = createVocabList(listOPosts)
    trainMat = []
    for postingDoc in listOPosts:
        trainMat.append(setOfWords2Vec(myVocabList, postingDoc))
    p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))
    testEntry = ['love', 'my', 'dalmation']
    thisDoc = array(setOfWords2Vec(myVocabList,testEntry))
    print testEntry,'classified as',classifyNB(thisDoc,p0V,p1V,pAb)
    testEntry = ['stupid', 'garbage']
    thisDoc = array(setOfWords2Vec(myVocabList,testEntry))
    print testEntry,'classified as:',classifyNB(thisDoc,p0V,p1V,pAb)
